Understanding the molecular characteristics of immune cells, such as memory cells, is critical for understanding adaptive immune response. In particular, gaps remain concerning our understanding of whether metabolic changes can drive T cell differentiation (and if so, how), or rather, if these changes are simply by-products of responses to external factors (e.g., cytokines) encountered during the course of differentiation. Further, it remains unclear whether there exists a direct causal relationship between modulation of the fatty acid metabolizing machinery and the critical cell fate decision(s) dictating the transition from an effector to a memory cell, and whether metabolic states in individual cells can be correlated with larger population dynamics. We seek to identify a panel of molecular markers will allow future functional testing and identification of fate decision pathways for this criticaly important cell type. To overcome the limitations of single cell molecular profiling and standard metabolic assays, we will develop a novel microfluidics device that will enable rapid single cell metabolic profiling and sorting coupled to single cell transcriptome profiling. Utilizing this devie, we will isolate rare subpopulations of CD8 T cells by their metabolic signatures and identify molecular markers for each subpopulation. In Aim 1, we will develop a microfluidics system for rapid single cell metabolic profiling and sorting. Specifically, a high-speed fluorocarbon (FC) oil droplet cell encapsulating microfluidics device coupled to metabolic functional dye probes and high-content detector sorting system will be developed. The device will be optimized for speed, capture efficiency, cell viability, high-speed detection, and sorting. In Aim 2, we will identify molecular markers in sub-population of CD8 T cells during response program using single cell RNASeq. To achieve this aim, we will couple the microfluidics device with select functional dyes to characterize individual cell's metabolic states. The profiles of the metabolic readouts will be analyzed by computational methods to identify distinct subpopulations. These metabolic signatures will be used to gate the cells by the microfluidics device in a sorting mode and the resulting subpopulations of cells will be analyzed by single cell RNASeq to identify molecular markers for each subtype. The results of this study will lead to future studies on the role of identified molecules in T cell response dynamics. Understanding the molecular basis of T cell functional subtypes will not only enhance understanding of our body's response to diseases, it will lead to translational applications in areas such as cancer immune-therapy.